Abstract: Disclosures herein teach applying a set of sections spanning a down-sampled version of an image of a road-scene to a low-fidelity classifier to determine a set of candidate sections for depicting one or more objects in a set of classes. The set of candidate sections of the down-sampled version may be mapped to a set of potential sectors in a high-fidelity version of the image. A high-fidelity classifier may be used to vet the set of potential sectors, determining the presence of one or more objects from the set of classes. The low-fidelity classifier may include a first Convolution Neural Network (CNN) trained on a first training set of down-sampled versions of cropped images of objects in the set of classes. Similarly, the high-fidelity classifier may include a second CNN trained on a second training set of high-fidelity versions of cropped images of objects in the set of classes.

Abstract: A device that includes a node engine configured to emulate a first node, a second node, and a third node. The first node is configured to receive a first correlithm object, fetch a second correlithm object based on the first correlithm object, and output the second correlithm object to the second node and the third node. Each correlithm object is a point in an n-dimensional space represented by a binary string. The second node is configured to receive the second correlithm object, fetch a third correlithm object based on the second correlithm object, and output the third correlithm object to the third node. The third node is configured to receive the second correlithm object, receive the third correlithm object, fetch a fourth correlithm object based on the second correlithm object and the third correlithm object, and output the fourth correlithm object.

Abstract: Provided is an object detection device for efficiently and simply selecting an image for creating instructor data on the basis of the number of detected objects. The object detection device is provided with: a detection unit for detecting an object from each of a plurality of input images using a dictionary; an acceptance unit for displaying, on a display device, a graph indicating the relationship between the input images and the number of subregions in which the objects are detected, and displaying, on the display device, in order to create instructor data, one input image among the plurality of input images in accordance with a position on the graph accepted by operation of an input device; a generation unit for generating the instructor data from the input image; and a learning unit for learning a dictionary from the instructor data.

Abstract: In an optical character recognition (OCR) method for digitizing printed text images using a long-short term memory (LSTM) network, text images are pre-processed using a stroke-aware max-min pooling method before being fed into the network, for both network training and OCR prediction. During training, an average stroke thickness is computed from the training dataset. Stroke-aware max-min pooling is applied to each text line image, where minimum pooling is applied if the stroke thickness of the line is greater than the average stroke thickness, while max pooling is applied if the stroke thickness is less than or equal to the average stroke thickness. The pooled images are used for network training. During prediction, stroke-aware max-min pooling is applied to each input text line image, and the pooled image is fed to the trained LSTM network to perform character recognition.

Abstract: A method for learning a runtime input transformation of real images into virtual images by using a cycle GAN capable of being applied to domain adaptation is provided. The method can be also performed in virtual driving environments. The method includes steps of: (a) (i) instructing first transformer to transform a first image to second image, (ii-1) instructing first discriminator to generate a 1_1-st result, and (ii-2) instructing second transformer to transform the second image to third image, whose characteristics are same as or similar to those of the real images; (b) (i) instructing the second transformer to transform a fourth image to fifth image, (ii-1) instructing second discriminator to generate a 2_1-st result, and (ii-2) instructing the first transformer to transform the fifth image to sixth image; (c) calculating losses. By the method, a gap between virtuality and reality can be reduced, and annotation costs can be reduced.

Abstract: An image processing device for evaluating a classifier which recognizes a target object within an input image and classifies the target object into one among a plurality of classes, includes: a classification unit which obtains a classification performance of the classifier by using the classifier and classifying the target object included in each of a plurality of verification images in which a class of the target object is known, and outputting one among the plurality of classes for each of the verification images; and an evaluation unit which outputs an evaluation result of the classifier based on the classification performance of the classifier obtained with the classification unit.

Abstract: A method for verifying an integrity of one or more parameters of a convolutional neural network (CNN) by using at least one test pattern to be added to at least one original input is provided for fault tolerance, fluctuation robustness in extreme situations, functional safety on the CNN, and annotation cost reduction. The method includes steps of: (a) a computing device instructing at least one adding unit to generate at least one extended input by adding the test pattern to the original input; (b) the computing device instructing the CNN to generate at least one output for verification by applying one or more convolution operations to the extended input; and (c) the computing device instructing at least one comparing unit to verify the integrity of the parameters of the CNN by determining a validity of the output for verification with reference to at least one output for reference.

Abstract: A method of learning for deriving virtual feature maps from virtual images, whose characteristics are same as or similar to those of real feature maps derived from real images, by using GAN including a generating network and a discriminating network capable of being applied to domain adaptation is provided to be used in virtual driving environments. The method includes steps of: (a) a learning device instructing the generating network to apply convolutional operations to an input image, to thereby generate a output feature map, whose characteristics are same as or similar to those of the real feature maps; and (b) instructing a loss unit to generate losses by referring to an evaluation score, corresponding to the output feature map, generated by the discriminating network. By the method using a runtime input transformation, a gap between virtuality and reality can be reduced, and annotation costs can be reduced.

Abstract: A method for acquiring a sample image for label-inspecting among auto-labeled images for learning a deep learning network, optimizing sampling processes for manual labeling, and reducing annotation costs is provided. The method includes steps of: a sample image acquiring device, generating a first and a second images, instructing convolutional layers to generate a first and a second feature maps, instructing pooling layers to generate a first and a second pooled feature maps, and generating concatenated feature maps; instructing a deep learning classifier to acquire the concatenated feature maps, to thereby generate class information; and calculating probabilities of abnormal class elements in an abnormal class group, determining whether the auto-labeled image is a difficult image, and selecting the auto-labeled image as the sample image for label-inspecting. Further, the method can be performed by using a robust algorithm with multiple transform pairs.

Abstract: According to an embodiment, a pattern recognition device is configured to divide an input signal into a plurality of elements, convert the divided elements into feature vectors having the same dimensionality to generate a set of feature vectors, and evaluate the set of feature vectors using a recognition dictionary including models corresponding to respective classes, to output a recognition result representing a class or a set of classes to which the input signal belongs. The models each include sub-models each corresponding to one of possible division patterns in which a signal to be classified into a class corresponding to the model can be divided into a plurality of elements. A label expressing a model including a sub-model conforming to the set of feature vectors, or a set of labels expressing a set of models including sub-models conforming to the set of feature vectors is output as the recognition result.

Abstract: A data processing method processes data using a processor including N (N is an integer equal to or more than 2) cores and a memory. The data processing method includes: searching a new character as a character whose raster data is not stored in the memory among a plurality of characters specified by character data included in print data to count a count of the searched new characters; generating M (M is an integer equal to or less than the N) threads, wherein the M is a count determined based on the count of the counted new characters among the N; dividing and allocating the plurality of characters to the M threads to generate raster data for the new characters using any one of the N cores for each of the M threads; and rendering the plurality of characters using raster data corresponding to the character data.

Abstract: In a case where a dot forming condition is not satisfied, whether to form a dot for a target pixel is determined according to a predetermined method and dependently on a colorant-corresponding gradation value for the target pixel. The colorant-corresponding gradation value for each pixel is defined for a color component corresponding to colorant and representing density of the color component in color of the each pixel. The dot forming condition includes that the target pixel is an edge pixel and that a density index value for the target pixel, which is related to density of the color component in the color of the target pixel that is represented by the colorant-corresponding gradation value, is greater than a threshold value. In a case where a dot forming condition is satisfied, it is determined that a dot is to be formed for the target pixel, irrespective of the colorant-corresponding gradation value.

Abstract: A movement control device configured to determine whether the moving unit moved by a driving unit is in a stopped state at a position before a predetermined target stop position, and whether a difference between the detection position and the target stop position is equal to or smaller than a threshold difference, based on a detection position of the moving unit, after the moving unit starts deceleration operation, and switch a control of the driving unit to a position control based on the difference between the detection position and the target stop position, from a speed control based on a designated speed associated with the detection position to the target stop position, if the moving unit is in the stopped state at a position before the predetermined target stop position, and the difference is equal to or smaller than the threshold difference.

Abstract: A printer is configured with a processor which calculates printhead control parameters, the control parameters being determined by the processor in such a way as to optimize the printing process of the printer's printhead. The processor determines the printhead control parameters according to an optimization algorithm stored in the printer. To maintain the internal security of the optimization algorithm, the printhead control parameters are encrypted by the processor. The encrypted printhead control parameters are then transmitted to the printhead via an internal data path of the printer. If a third party monitors the data along the internal data path, the encryption algorithm remains secure because the control parameters are encrypted. The printhead contains a second, dedicated processor. The printhead processor receives the encrypted printhead control parameters, and decrypts the control parameters.

Abstract: A method is disclosed for creating a 2D barcode. In an embodiment, the method includes: embedding primary information that can be read by a 2D-barcode-reader in a primary information pattern, and embedding secondary information that is made difficult to reproduce without alteration in a visible pattern. In an embodiment, the visible pattern is embedded within the barcode in at least one area that does not contain any primary information.

Abstract: A system for generating a two-dimensional barcode picture having a dynamic effect may receive a target character string and encode the target character string into a two-dimensional barcode sequence. The system may obtain a feature bit in the two-dimensional barcode sequence and calculating relative coordinates of the feature bit in a two-dimensional barcode specification matrix. The system may obtain a preset drawing area and map the feature bit into a feature area in the drawing area according to the relative coordinates of the feature bit in the two-dimensional barcode specification matrix. The system may draw a dynamic picture in the drawing area by using a canvas component according to a preset animation effect definition. An image frame of the dynamic picture at the end of an animation effect being an image frame having the feature area configured with a pixel whose grayscale value is a standard grayscale value corresponding to the feature bit.

Abstract: A method and system for determining a position of a camera is disclosed. The method and system includes determining and registering geographical coordinates of a mobile device in the mobile device itself, presenting on a display of the mobile device a pattern representing the geographical coordinates of the mobile device, capturing by the camera an image of the display of the mobile device when presenting the geographical coordinates, translating in the camera the pattern in the captured image of the display of the mobile device into geographical coordinates, and determining in the camera the position of the camera based on the geographical coordinates translated from the pattern in the captured image.

Abstract: A remote control for an air conditioner facilitates setting of various kinds of data without operation of a button. A remote control (30) includes an external communication unit (45) having an NFC circuit (42) and an NFC antenna (43). The external communication unit (45) is capable of wirelessly communicating with an external apparatus even when a microcomputer (36) in the remote control (30) is not supplied with power (i.e., in a non-operating state). Hence, through the wireless communication between the external communication unit (45) of the remote control (30) and the external apparatus, the various kinds of data is forwarded from the external device to the remote control (30), and stored in the remote control (30).

Abstract: An RFID transponder (T) suitable for communication with a reading device (RD) and adapted to be connected to a monitored unit (MU) is provided. The RFID transponder (T) comprises a comparing unit (CU) adapted to and arranged to receive a status signal from the monitored unit (MU) and configured to compare a value of the status signal to at least one predefined reference value and a state machine circuit (STM) connected to the comparing unit (CU) and configured to determine, based on a result of the comparison, whether the value of the status signal lies outside a range of operation defined by the at least one reference value. The state machine circuit (STM) is further configured to indicate the reading device (RD) that the value of the status signal lies outside the range of operation if the value of the status signal lies outside the range of operation.

Abstract: RFID tags may compensate for non-RFID power sources by automatically enforcing data or state persistence even while powered. A tag may measure a time interval between successive detected reader commands. If the interval exceeds a minimum time, then the tag may deassert a protocol flag, erase data, and/or change tag operating states, even if the tag would normally not perform these actions while powered.

Abstract: The utility tracker taught by the present invention is a BLUETOOTH low energy tag, plus a three axis accelerometer which is approximately the size of a quarter. The utility tracker of the present invention can be secured, typically by “sticking” the dot to an asset by releasable glue or tape. The utility tracker may be stuck to any asset, which could be a fiber optic cable, or any other non-metallic asset that get placed by a utility company, and which can subsequently be detected by a mobile device.

Abstract: Systems and methods for indicating tampering of a stored value card. The systems generally include a stored value card having a first activation indicia, and a removable label having a second activation indicia and covering the first activation indicia. To activate the stored value card, the first and second activation indicia are scanned. The removable label is configured to render the second activation indicia unreadable and/or to provide visual evidence of tampering such that activation is not recommended.

Abstract: Embodiments disclosed herein generally relates to a payment card, and more specifically to a payment card with obfuscated personal identification information and a method of generating the same. In one embodiment, an apparatus is disclosed herein. The apparatus includes a substrate, one or more sets of personal identification information, and a masking layer. The substrate is formed from a water-insoluble material. The one or more sets of personal identification information is formed on the substrate. The masking layer is selectively positioned on the substrate to obscure at least one set of personal identification information. The masking layer is formed from a water-soluble material.

Abstract: A radio IC device includes an electromagnetic coupling module includes a radio IC chip arranged to process transmitted and received signals and a feed circuit board including an inductance element. The feed circuit board includes an external electrode electromagnetically coupled to the feed circuit, and the external electrode is electrically connected to a shielding case or a wiring cable. The shielding case or the wiring cable functions as a radiation plate. The radio IC chip is operated by a signal received by the shielding case or the wiring, and the answer signal from the radio IC chip is radiated from the shielding case or the wiring cable to the outside. A metal component functions as the radiation plate, and the metal component may be a ground electrode disposed on the printed wiring board.

Abstract: A Non-transferable Radio Frequency Identification (RFID) assembly for attachment to an article comprises a RFID module; and a antenna module coupled with the RFID module, the antenna module comprising a conductive layer, a substrate, and an adhesive modification layer between the conductive layer and the substrate, the adhesive modification layer configured such that when the assembly is attached to the article and attempt to remove the assembly will cause the substrate to release and leave the conductive layer intact.

Abstract: An RFID device includes an antenna and an RFID strap. The RFID strap is coupled to the antenna by a combination of magnetic and electric fields. The RFID strap includes an RFID chip and a strap conductor coupling the RFID chip to the antenna. The strap conductor has a loop section positioned generally adjacent to the antenna to magnetically couple the RFID strap to the antenna. The strap conductor also has an extension section overlapping and crossing the antenna to electrically couple the RFID strap to the antenna. By adjusting the size of the RFID strap, the configuration of the RFID strap, the degree of overlap between the extension section and the antenna, and/or the angular orientation of the RFID strap with respect to the antenna, the impedance transformation between the RFID chip and the antenna may be varied to better match the RFID chip and the antenna.

Abstract: Client-server hybrid A.I. scores for customized actions are described. A client generates client scores corresponding to client customized actions by applying a user-specific model to an action received from a user, the user-specific model based on at least one historical action received from the user. The client requests a server to provide server scores corresponding to server customized actions by applying a cross-user model to the action received from the user, the cross-user model based on historical actions associated with server users. The client generates hybrid scores corresponding to hybrid customized actions by combining the client scores with the server scores, in response to receiving the server scores from the server. The client causes the hybrid customized actions to be outputted based on the corresponding hybrid scores.

Abstract: Systems and methods are provided for automatically scoring a constructed response. The constructed response is processed to generate a plurality of numerical vectors that is representative of the constructed response. A model is applied to the plurality of numerical vectors. The model includes an input layer configured to receive the plurality of numerical vectors, the input layer being connected to a following layer of the model via a first plurality of connections. Each of the connections has a first weight. An intermediate layer of nodes is configured to receive inputs from an immediately-preceding layer of the model via a second plurality of connections, each of the connections having a second weight. An output layer is connected to the intermediate layer via a third plurality of connections, each of the connections having a third weight. The output layer is configured to generate a score for the constructed response.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for organizing trained and untrained neural networks. In one aspect, a neural network device includes a collection of node assemblies interconnected by between-assembly links, each node assembly itself comprising a network of nodes interconnected by a plurality of within-assembly links, wherein each of the between-assembly links and the within-assembly links have an associated weight, each weight embodying a strength of connection between the nodes joined by the associated link, the nodes within each assembly being more likely to be connected to other nodes within that assembly than to be connected to nodes within others of the node assemblies.

Abstract: Systems, methods, and apparatus, including computer programs encoded on a computer storage medium for processing a network input through a neural network having one or more initial neural network layers followed by a softmax output layer. In one aspect, the methods include obtaining a layer output generated by the one or more initial neural network layers and processing the layer output through the softmax output layer to generate a neural network output. Processing the layer output through the softmax output layer includes determining, for each possible output value, a number of occurrences in the layer output values; for each possible output value occurring in the layer output values, determining a respective exponentiation measure; determining a normalization factor for the layer output by combining the exponentiation measures in accordance with the number of occurrences of the possible output values; and determining, for each of layer output values, a softmax probability value.

Abstract: A method of quantizing a floating point machine learning network to obtain a fixed point machine learning network using a quantizer may include selecting at least one moment of an input distribution of the floating point machine learning network. The method may also include determining quantizer parameters for quantizing values of the floating point machine learning network based at least in part on the at least one selected moment of the input distribution of the floating point machine learning network to obtain corresponding values of the fixed point machine learning network.

Abstract: Embodiments are directed to a two-terminal resistive processing unit (RPU) having a first terminal, a second terminal and an active region. The active region effects a non-linear change in a conduction state of the active region based on at least one first encoded signal applied to the first terminal and at least one second encoded signal applied to the second terminal. The active region is configured to locally perform a data storage operation of a training methodology based at least in part on the non-linear change in the conduction state. The active region is further configured to locally perform a data processing operation of the training methodology based at least in part on the non-linear change in the conduction state.

Abstract: A method of tracking an object across a stream of images comprises determining a region of interest (ROI) bounding the object in an initial frame of an image stream. A HOG map is provided for the ROI by: dividing the ROI into an array of M×N cells, each cell comprising a plurality of image pixels; and determining a HOG for each of the cells. The HOG map is stored as indicative of the features of the object. Subsequent frames are acquired from the stream of images. The frames are scanned ROI by ROI to identify a candidate ROI having a HOG map best matching the stored HOG map features. If the match meets a threshold, the stored HOG map indicative of the features of the object is updated according to the HOG map for the best matching candidate ROI.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for receiving, by a computational graph system, a request to process a computational graph; obtaining data representing a subgraph of the computational graph, the computational graph comprising a plurality of nodes and directed edges, wherein each node represents a respective operation, wherein each directed edge connects a respective first node to a respective second node, the subgraph assigned to a first device by a placer in the computational graph system; determining that the first device comprises a hardware accelerator having a plurality of streams; in response to determining, generating instructions that when executed by the first device cause the first device to: assign the operation represented by each node in the subgraph to a respective stream; and perform the operations represented by the nodes in the subgraph in accordance with the assignment.

Abstract: Systems and methods for training a neural network to optimize network performance, including sampling an applied dropout rate for one or more nodes of the network to evaluate a current generalization performance of one or more training models. An optimized annealing schedule may be generated based on the sampling, wherein the optimized annealing schedule includes an altered dropout rate configured to improve a generalization performance of the network. A number of nodes of the network may be adjusted in accordance with a dropout rate specified in the optimized annealing schedule. The steps may then be iterated until the generalization performance of the network is maximized.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a variational auto-encoder (VAE) to generate disentangled latent factors on unlabeled training images. In one aspect, a method includes receiving the plurality of unlabeled training images, and, for each unlabeled training image, processing the unlabeled training image using the VAE to determine the latent representation of the unlabeled training image and to generate a reconstruction of the unlabeled training image in accordance with current values of the parameters of the VAE, and adjusting current values of the parameters of the VAE by optimizing a loss function that depends on a quality of the reconstruction and also on a degree of independence between the latent factors in the latent representation of the unlabeled training image.

Abstract: During training mode, first input data is provided to a first neural network to generate first output data indicating that the first input data is classified in a first cluster. The first input data includes at least one of a continuous feature or a categorical feature. Second input data is generated and provided to at least one second neural network to generate second output data. The at least one second neural network corresponds to a variational autoencoder. An aggregate loss corresponding to the second output data is determined, including at least one of evaluating a first loss function for the continuous feature or evaluating a second loss function for the categorical feature. Based on the aggregate loss, at least one parameter of at least one neural network is adjusted. During use mode, the neural networks are used to determine cluster identifications and anomaly likelihoods for received data samples.

Abstract: Mechanisms, in a system comprising a host system and at least one accelerator device, for performing a concept analysis operation are provided. The host system extracts a set of one or more concepts from an information source and provides the set of one or more concepts to the accelerator device. The host system also provides at least one matrix representation data structure representing a graph of concepts and relationships between concepts in a corpus. The accelerator device executes the concept analysis operation internal to the accelerator device to generate an output vector identifying concepts in the corpus, identified in the at least one matrix representation data structure, related to the set of one or more concepts extracted from the information source. The accelerator device outputs the output vector to the host system which utilizes the output vector to respond to a request submitted to the host system associated with the information source.

Abstract: An analytics processing system generates analytics from a collection of unstructured data by identifying trends in the data and deriving associations or correlations between series of values. Each series is generated from a set of field labeled values in the set, and compared to other series in the collection. Identified relationships in the series are scored based on depiction of an illustrative, predictive, or non-random association, and ranked by a scoring metric for analytical value. A visualization of the relationships are ranked and rendered such that the visualization highlights the association in a manner not achievable by simple inspection of the field values. Relationships are graphed by lines, circles, bars (histogram) on labeled axes based on the series. In this manner, a user may generate analytic results from a large data set, and pinpoint significant associations by paging through renderings scored as the most illustrative of notable trends.

Abstract: According to an example aspect of the present invention, there is provided an apparatus comprising at least one processing core, at least one memory including computer program code, the at least one memory and the computer program code being configured to, with the at least one processing core, cause the apparatus at least to determine a predicted user activity type based at least partly on a thematic map database and a current location of the apparatus, present the predicted user activity type as a suggested activity type to a first user, and responsive to the first user approving the suggested activity type, initiate an activity session of the suggested activity type.

Abstract: An approach is provided in which a knowledge manager creates a pattern set that includes paraphrases and corresponding paraphrase scores. The paraphrase scores are based on a set of first candidate answers obtained from querying a first set of resource data. The knowledge manager performs a search, which is based on the paraphrases and a user question, on a second set of resource data and identifies a set of second candidate answers. In turn, the knowledge manager scores the set of second candidate answers based on the paraphrase scores corresponding to the paraphrases utilized to identify the set of second candidate answers.

Abstract: A predictive estimator, trained on a data corpus, is used to generate a probability estimate based a sequence of data related to an entity. The predictive estimator computes an instantaneous surprise score which is a quantification of a short-term deviation of a datum from the probability estimate. To compute the instantaneous surprise score, the predictive estimator is initialized with default values of the predictive estimator. Then, for each of data input of the datum to the predictive estimator, the instantaneous surprise score is calculated, corresponding to the deviation of the data input from the probability estimate. This generates an estimate of the probability of observing the datum given past data on the entity and the predictive estimator. The predictive estimator is updated with the datum and the time step advanced.

Abstract: Techniques are disclosed for generating a sequence of symbols based on input data for a neuro-linguistic model. The model may be used by a behavior recognition system to analyze the input data. A mapper component of a neuro-linguistic module in the behavior recognition system receives one or more normalized vectors generated from the input data. The mapper component generates one or more clusters based on a statistical distribution of the normalized vectors. The mapper component evaluates statistics and identifies statistically relevant clusters. The mapper component assigns a distinct symbol to each of the identified clusters.

Abstract: In a search schedule determining apparatus, a target probability distribution showing a distribution of probabilities of positions of a target in a time zone is calculated based on target previous information. A search ability showing a quantitative evaluation of an ability to discover the target is calculated based on search vehicle previous information. A search effort distribution is calculated based on the target probability distribution and the search ability such that a combination of probabilities in which each of the search vehicles discovers the target has a maximum. A search schedule is automatically determined based on the target previous information and the search vehicle previous information such that the search vehicles are operated to realize the search effort distribution.

Abstract: A method and system adaptively improves potential customer conversion rates, revenue metrics, and/or other target metrics by providing effective user experience options, from a variety of different user experience options, to some users while concurrently testing user responses to other user experience options, according to one embodiment. The method and system selects the user experience options by applying user characteristics data to an analytics model, according to one embodiment. The characteristics of the analytics model are adjusted and/or tuned to control/reduce uncertainty in identifying effective user experience options, according to one embodiment. The method and system analyzes user responses to the user experience options to update the analytics model, and to dynamically adapt the personalization of the user experience options, at least partially based on feedback from users, according to one embodiment.

Abstract: A method, system, and computer program product for generating database cluster health alerts using machine learning. A first database cluster known to be operating normally is measured and modeled using machine learning techniques. A second database cluster is measured and compared to the learned model. More specifically, the method collects a first set of empirically-measured variables of a first database cluster, and using the first set of empirically-measured variables a mathematical behavior predictor model is generated. Then, after collecting a second set of empirically-measured variables of a second database cluster over a plurality of second time periods, the mathematical behavior predictor model classifies the observed behavior. The classified behavior might be deemed to be normal behavior, or some form of abnormal behavior. The method forms and report alerts when the classification deemed to be anomalous behavior, or fault behavior.

Abstract: Various implementations for simplified product configuration using table-based rule editing, rule conflict resolution through voting, and efficient model compilation are described. In one example implementation, a rule definition table is provided for presentation to a user. One or inputs defining a rule for a model using the rule definition table are received. The rule is compiled into a compiled rule that is executable during evaluation of the model and the model is evaluated based on the compiled rule. Numerous additional implementations are also described.

Abstract: The disclosed embodiments provide a system for facilitating sentiment analysis. During operation, the system obtains a set of training data that includes a first set of content items containing words associated with a domain, a set of sentiment scores for the first set of content items, and a set of outcomes associated with the first set of content items. Next, the system uses the training data to train a statistical model for performing sentiment analysis that is specific to the domain. The system then enables use of the statistical model in generating a set of domain-specific sentiment scores for a second set of content items containing words associated with the domain.

Abstract: A method, system, and computer program product for weight adjusted composite model for forecasting in anomalous environments are provided in the illustrative embodiments. A base forecasting model and a second forecasting model are combined to form a composite model, the base forecasting model configured to forecast an event in a time series, the second forecasting model configured to represent an anomalous portion of data in the time series. A mixing algorithm is combined with the composite model to adjust a set of weights associated with the composite model. Upon identifying a future period in which the event is to be forecasted, using the mixing algorithm, a subset of the set of weights is adjusted to from a weight adjusted composite model. The weight adjusted composite model is executed to forecast the event in the future period.

Abstract: Technologies for platform-targeted machine learning include a computing device to generate a machine learning algorithm model indicative of a plurality of classes between which a user input is to be classified and translate the machine learning algorithm model into hardware code for execution on the target platform. The user input is to be classified as being associated with a particular class based on an application of one or more features to the user input, and each of the one or more features has an associated implementation cost indicative of a cost to perform on a target platform on which the corresponding feature is to be applied to the user input.